Security Breaches Research Articles

Security and Privacy Issues in Internet of Things (IoT) Devices Using COPRAS Method

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

PP55 Citizens’ General Needs Assessment In SOTERIA Project: ‘User-friendly Digital Secured Personal Data And Privacy Platform’

IntroductionDuring the COVID-19 pandemic, the use of e-services increased, bringing a better choice of technologies and services. Nevertheless, these services require the use of sensitive personal data, which increased the level of risk because of potential privacy and security breaches. As a result, citizens are more concerned about their privacy, security, and personal data protection. The SOTERIA project aims to provide a secure and user-driven solution. This study aims to identify barriers and key points related to citizens when using digital systems for data interoperability.MethodsBoth qualitative and quantitative approaches were applied in order to identify and measure citizens’ general needs regarding the tool to be developed within the context of SOTERIA. The questionnaire was distributed throughout the three European countries under study: Austria, Romania, and Spain, with 400 responses collected in each country. The distribution of sex, age, occupation and educational level was representative in the entire sample.ResultsThis study corroborates the critical role of perceived security in the intention to adopt new technologies. In addition, to trust and being consistent with the extant literature on technology adoption/acceptance, perceived benefits and usefulness also play a crucial role in driving attitudes and behavioral intention to adopt digital data wallet (DDW). Our findings show that perceived ease of use, the user’s belief of having no difficulty using the technology (i.e., DDW), is a significant predictor of consumers’ intentions to use DDW. Our qualitative data from both the in-depth interviews and focus groups also revealed convenience, being comfortable, and/or less time and energy needed to use DDW in comparison to one’s current situation, to be a determinant of perceived benefits. Transparency, which provides consumers with knowledge of how firms manage their personal information, was also viewed as important among our participants in both in-depth interviews and focus groups. Our findings corroborate previous studies that report the control of privacy concerns, or the extent to which consumers believe they can manage the flow of information, feel comfortable and enhance their perceived view of privacy or lower privacy concerns.ConclusionsOur qualitative studies confirmed that trust, or consumers’ expectation of how data will be handled in the future plays an important role in influencing the intention of DDW adoption.

Anomaly detection using deep convolutional generative adversarial networks in the internet of things

Advanced 5 G and 6 G technologies have accelerated the adoption of the Internet of Things (IoT) and are a priority in providing support for high-speed communication and fast data analysis. One of IoT networks benefits is automated networking, which unfortunately increases the risk of security, integrity, and privacy breaches. Therefore, in this paper, we propose a weighted stacked ensemble model combining deep convolutional generative adversarial and bidirectional long short-term memory networks. The proposed model has been regularized, and hyperparameter tuning has been performed. The tuned model is then evaluated on four publicly available current IoT datasets. The proposed model exhibits significant improvement in standard performance measures for both binary and multiclass classification. Generalization error has been reduced by a rate of 0.005% and to overcome the issue of overfitting, a L2 regularization technique has been deployed. The overall Accuracy of the model on various datasets is 99.99% for BOT-IoT, 99.08% for IoT23, 99.82% for UNSWNB15, and 99.96% for ToN_IoT, respectively, alongside improvements in Precision, Recall, and F1-score.

Managerial Implications for Adopting Advanced Biometric Authentication: A Case Study on Optimizing Keystroke Dynamics Using ISKKC

This paper offers a technology management lens for deploying and optimising advanced biometric authentication systems, utilising the Integrated SVM-KNN Keystroke Classifier (ISKKC) model as a case study. The ISKKC system employs an innovative machine learning stack, including regression classifiers, k Nearest Neighbors (KNN), and Support Vector Machines (SVM), to amplify the accuracy of keystroke dynamics, especially in multi-lingual settings. Utilising a comprehensive research design, we gathered and analysed data from 60 participants who are native speakers of either Malay or Chinese. The study's findings substantiate that ISKKC surpasses standalone authentication models in various performance metrics such as accuracy, precision, recall, and specificity. For technology managers, these results translate into strategic advantages, including enhanced system reliability, lower risk of security breaches, and higher overall Return On Investment (ROI). The study also explores managerial considerations such as the ease of ISKKC's integration into existing technology infrastructures, the costs associated with its implementation, and the strategic alignment with organisational objectives. Furthermore, we discuss how the ISKKC model can contribute to a sustainable competitive advantage by offering superior authentication security, facilitating compliance with regulatory standards and customer trust in an increasingly digital ecosystem. This research aims to guide technology managers to make informed decisions on adopting and optimising biometric authentication systems, emphasising the operational and strategic advantages conferred by machine learning algorithms like those incorporated in ISKKC.

Enhancing Blockchain Technology Platforms: Leveraging Artificial Intelligence for Optimal Support and Efficiency

Recently, the landscape of e-commerce has undergone a remarkable transformation, propelled by a sudden surge in blockchain technology. The traditional era of bank transactions was plagued by technical difficulties, transfer limitations, security breaches, and high transfer fees. However, blockchain technology, which utilizes a decentralized public database for storing and transferring funds via cryptocurrencies, has emerged as a promising solution. This literature review delves into the multifaceted challenges associated with blockchain technology and explores various works addressing these concerns. Additionally, this study investigates the potential of integrating artificial intelligence to tackle the obstacles faced by blockchain technology, paving the way for an even more efficient and secure e-commerce ecosystem.

A Comprehensive Overview of the Unequivocal Influence and Evolution of Cryptography on Security

Abstract: As this society unfolds into a conundrum of new possibilities and technical advancements, it simultaneously sets ground to several unprecedented challenges for its users. What today’s digitally ensnared society doesn’t understand is its vulnerability to malicious software, cyber attacks, and innumerable privacy violations that come their way as they mold themselves into this digital realm. For a country like India, that well adapts to the newer technologies invented across the globe, it is astonishing to see almost 1.3 million cybersecurity breaches being recorded in 2022, a hideous spike from 2019. Current internet users feel the need to be active on such delusional platforms every minute of their day, exposing themselves at every stage. This empowers unethical practices and practitioners to lure unsuspecting candidates into their traps and obtain private information linked to several sensitive, especially monetary affairs of the individual, consequently resulting in severe financial losses. Ironically, the very technological influence that facilitates security breaches is one that helps in its deterrence and prevention. One of the most predominant and recent technologies that have empowered its influence over such prevention is cryptography and encryption. Traditionally, prior to the widespread awareness about cryptography, companies and individuals used Access Control Lists(ACLs) that work on the basic principle of restricting or revoking access on certain administrative grounds offering data confidentiality and security on the internet. Apart from these companies, in the ancient times, there was significant use of Caesar Cipher, a way of encryption that works on the principle of substitution by a shift of some number down the alphabet. These traditional methods lacked transparency, had increased complexity, were insufficient for rapidly growing networks such as online social media, and also lacked scalability. In order to compensate for these challenges, a newer technology concatenated with cryptography is to be made prevalent in society so that it can offer more scalable and trustworthy encryption and security prevention from online security breaches across the globe. With this ideology, prevention and awareness both can be obtained and a better and safer society can be established in this generation.

Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography

The safeguarding of intellectual property and maintaining privacy for video content are closely linked to the effectiveness of security protocols employed in internet streaming platforms. The inadequate implementation of security measures by content providers has resulted in security breaches within entertainment applications, hence causing a reduction in the client base. This research aimed to enhance the security measures employed for video content by implementing a multi-key approach for encryption and decryption processes. The aforementioned objective was successfully accomplished through the use of hybrid methodologies, the production of dynamic keys, and the implementation of user-attribute-based techniques. The main aim of the study was to improve the security measures associated with the process of generating video material. The proposed methodology integrates a system of mathematical equations and a pseudorandom key within its execution. This novel approach significantly augments the degree of security the encryption mechanism provides. The proposed methodology utilises a set of mathematical equations that are randomly employed to achieve encryption. Using a random selection procedure contributes to the overall enhancement of the system’s security. The suggested methodology entails the division of the video into smaller entities known as chunks. Following this, every segment is subjected to encryption using unique keys that are produced dynamically in real-time. The proposed methodology is executed via Android platforms. The transmitter application is tasked with the responsibility of facilitating the streaming of the video content, whereas the receiver application serves the purpose of presenting the video to the user. A careful study was conducted to compare and contrast the suggested method with other similar methods that were already in use. The results of the study strongly support the safety and dependability of the procedure that was made available.

Detection of Shadow IT Incidents for Centralized IT Management in Enterprises using Statistical and Machine Learning Algorithms

Software as a Service (SaaS) is a software service where software solutions are offered to users via the internet, usually subscription-based or sometimes opened to access by selling a license key, distributed over the cloud, and updates are automatically delivered to users because they are distributed over the cloud. The number of SaaS provider companies is increasing day by day, and with this increase, unauthorized purchase of SaaS applications has become a problem for corporate-sized companies. Without the company's approval, SaaS software and hardware used by employees increase Shadow IT which means there is a potential risk of security breaches, data loss, and compliance issues as the IT department is unaware of the usage and unable to monitor and control the systems effectively. In this study, in order to avoid the problems that may be caused by Shadow IT, unauthorized SaaS applications in Arçelik Global have been detected by utilizing statistical and machine learning approaches. In the experiment, Interquartile Range, K-Means and Stabilization algorithms were used for the detection of unauthorized SaaS applications. Using all three algorithms, low, medium and high-risk shadow IT detection was made for Arçelik company. We see that the proposed stabilization approach explores unauthorized SaaS applications much more distinctively than the other two algorithms. The proposed approach can be used in the future to detect unauthorized software from other companies.

Revocation Speedrun: How the WebPKI Copes with Fraudulent Certificates

The TLS ecosystem depends on certificates to bootstrap secure connections. Certificate Authorities (CAs) are trusted to issue these correctly. However, as a result of security breaches or attacks, certificates may be issued fraudulently and need to be revoked prematurely. Revocation, as a reactive measure, is fundamentally damage control and, as such, time is critical. Therefore, measuring reaction delay is the first step to identifying how well the revocation system functions. In this paper we attempt to characterize the current performance of the WebPKI in dealing with fraudulent certificates. We present measurements of each step in the revocation process: the detection of certificate issuance through Certificate Transparency (CT) monitoring, the administrative revocation process at popular CAs, and the revocation checking behavior of end-user clients, both in a controlled virtualized environment and in the wild. We perform two live measurements, in 2022 and 2023, respectively, to provide a longitudinal comparison. We find that detection and revocation of fraudulent certificates is quick and efficient when leveraging CT and can be completed within 6.5 hours on average. Furthermore, CT is being increasingly enforced by some browsers. However, ∼83% of the clients we observed, across popular browsers, brands and OSes, completely disregard a certificate's status, whileall of the studied browsers still display soft-fail behavior, making them vulnerable to attackers capable of interfering with the network. Of the clients that do check revocation, we find that 35% can be made to accept a revoked certificate through the use of OCSP Stapling. We expect this number to grow with client-side adoption of OCSP Stapling [RFC6961]. Current OCSP expiration times allow a revoked certificate to remain fully valid for up to 7 days for the majority of CAs, exposing clients to attacks.

Modified Caesar Cipher and Card Deck Shuffle Rearrangement Algorithm for Image Encryption

ABSTRACT In today's social networking era, information security breaches are at an all-time high, especially when it comes to texts and images, which should come as no surprise. Integrity is therefore crucial for information sharing over an unprotected connection. As a result, image/text encryption is a crucial preprocessing step in today's information transmission. Sensitive information may be sent via image encryption, which facilitates a variety of procedures. Here the proposed method is a combination of the Modified Caesar Cipher and the Card Deck Shuffle Algorithm for image encryption. The Card Deck Shuffle Algorithm rearranges the pixels from the result of the Modified Caesar approach, which encrypts the pixel value of each image. The method employs an n + 8 variable bit key (n is frequently more than 18 in real-world applications), which needs more than 2 26 brute force attacks to be successful. The method described here may be used to secure images in a variety of multimedia applications.

Active eavesdropping detection: a novel physical layer security in wireless IoT

Considering the variety of Internet of Things (IoT) device types and access methods, it remains necessary to address the security challenges we currently encounter. Physical layer security (PLS) can offer streamlined security solutions for the next generation of IoT networks. Presently, we are witnessing the application of intelligent technologies including machine learning (ML) and artificial intelligence (AI) for precise prevention or detection of security breaches. Active eavesdropping detection is a physical layer security-based method that can differentiate wireless signals between wireless devices through feature classification. However, the operation of numerous IoT devices operate in environments characterized by low signal-to-noise ratios (SNR), and active eavesdropping attack detection during communication is rarely studied. We assume that the wireless system comprising an access point (AP), K authorized users and a proactive eavesdropper (E), following the framework of transforming wireless signals at AP into organized datasets that this article proposes a BP neural network model based on deep learning as a classifier to distinguish eavesdropping and non-eavesdropping attack signals. By conducting experiments under SNRs, the numerical results show that the proposed model has stronger robustness and detection accuracy can significantly improve the up to 19.58% compared with the reference approach, which show the superiority of our proposed method.

Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Privacy-Preserving k-Nearest Neighbor Classification over Malicious Participants in Outsourced Cloud Environments

In recent years, many companies have chosen to outsource data and other data computation tasks to cloud service providers to reduce costs and increase efficiency. However, there are risks of security and privacy breaches when users outsource data to a cloud environment. Many researchers have proposed schemes based on cryptographic primitives to address these risks under the assumption that the cloud is a semi-honest participant and query users are honest participants. However, in a real-world environment, users’ data privacy and security may be threatened by the presence of malicious participants. Therefore, a novel scheme based on secure multi-party computation is proposed when attackers gain control over both the cloud and a query user in the paper. We prove that our solution can satisfy our goals of security and privacy protection. In addition, our experimental results based on simulated data show feasibility and reliability.

Improved End-to-End Data Security Approach for Cloud Computing

Cloud computing is one of the major cutting-edge technologies that is growing at a gigantic rate to redefine computation through service-oriented computing. It has addressed the issue of owning and managing computational infrastructure by providing service through a pay-and-use model. However, a major possible hindrance is security breaches, especially when the sender uploads or the receiver downloads the data from a remotely accessed server. It is a very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposes an end-to-end data security approach from the sender side to the receiver side by adding extra padding sequences, as well as randomized salting, followed by hashing and an encryption technique. The effectiveness of the proposed method was established using both a simulated system and mathematical formulations with different performance metrics. Furthermore, its performance was compared with those of contemporary algorithms, showing that the proposed algorithm creates a larger ciphertext that is almost impossible to crack due to randomization modules. However, it has significantly longer encryption and decryption times, although our primary concern is ensuring security, not reducing time.

Blackbaud to pay states $49.5 million to settle data security breach case

Blackbaud to pay states $49.5 million to settle data security breach case

Utilizing Artificial Intelligence for Enhancing Cyber Security: Applications and Methodologies

The rapid advancement of technology has given rise to a host of security challenges, often leaving cybersecurity experts struggling to keep up with the latest developments. Safeguarding against security breaches and cyber-attacks now requires extensive support due to the intricate interconnections between organizations, resulting in a surge of network activity, heightened vulnerability, and an increased array of potential attack points. Addressing these complexities has become a daunting task for human capabilities alone. Conventional algorithms occasionally prove inadequate when dealing with the complexities of modern systems. The creation of software with an automatic updating mechanism, designed to stay aligned with technological progress, presents a formidable challenge. Within this landscape, Artificial Intelligence (AI) emerges as a promising avenue for mitigating cybersecurity concerns to a significant extent. By examining cybersecurity computational applications and analyzing strategies for enhancing cyber defense, AI-based solutions offer a potent pathway. This study delves into the realm of countering cybersecurity threats by harnessing the capabilities of AI applications and techniques. It explores how AI can fortify cybersecurity measures, proposing innovative approaches while also examining existing methodologies.

Digital Forensic Investigation Tools for Cases Related to Social Media and Cybersecurity

The entire world has moved into the digital realm as a result of the quickening pace of technological development. However, this change has also led to an increase in cybercrimes and security breach occurrences, which endanger user security and privacy. As a result, this study sought to examine how digital forensics, a significant advancement in cybersecurity, is used to combat cybercrime. The most recent developments in digital forensics, such as cloud forensics, social media forensics, and IoT forensics, have been examined in this study. With the use of these technologies, cybersecurity experts can protect data while identifying fraudsters by using the digital footprints that data processing and storage leave behind. Technical, operational, and personnel-related issues are among the specific dangers to digital forensics that have been identified by the research. These systems' high level of complexity, volume of data, chain of custody, personnel integrity, and the validity and accuracy of digital forensics are all significant barriers to their widespread adoption. However, the research has also noted the use of artificial intelligence, intrusion detection, and USB forensics as significant prospects for digital forensics that can make the processes simpler, more effective, and safe.

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection.

Facilitating and impeding factors to insiders’ prosocial rule breaking in South Korea

Insiders’ security breach in healthcare is a serious issue both to healthcare organizations and to patients. Investigating factors affecting prosocial intention to disclose committed by insiders helps better understand and manage these breaches in healthcare. This study identifies the factors, including contextual factors, personal factors, job factors, and security countermeasures. A research model was developed by incorporating factors that affect student nurse trainees’ security breaches. A scenario-based experiment was conducted to empirically test the model, with student nurse trainees in South Korea. This study found that one contextual factor and several personal factors affect the prosocial intention to disclose.

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events.